EP3343201B1 - Method and device for counting and characterising particles in a moving fluid - Google Patents

Description

TECHNICAL AREA

The technical field of the invention is the counting of particles circulating in a fluidic chamber using an optical method.

PRIOR ART

Several optical methods have already been implemented to count particles circulating in a fluid, the fluid being a gas or a liquid. A widespread principle is based on illumination, using a light beam, of a fluid in which particles are circulating. When a particle crosses the beam, part of the light is scattered and can be detected by a photodetector. This principle has been implemented for the detection of particles in the air, or for the detection of cells in liquids, for example biological liquids.

Other methods are based on the analysis of images, for example using a microscope, but the images only provide two-dimensional information as to the position of the particles.

The document WO2008090330 describes a device allowing the observation of samples comprising cells by lensless imaging. The sample is disposed between a light source and an image sensor, without disposing an image forming optical system between the sample and the image sensor. Thus, the image sensor collects an image, also called a hologram, formed of interference figures between the light wave emitted by the light source and transmitted by the sample, and diffraction waves, resulting from diffraction by the sample of the light wave emitted by the light source. These interference figures are generally formed by a succession of concentric rings. They are sometimes called diffraction figures, or designated by the English term "diffraction pattern". Images are thus acquired, the field of observation of which is clearly greater than that of a microscope. When the concentration of cells in the sample is sufficiently low, each cell can be associated with an interference figure; counting them allows the counting of the cells present in the sample. But the hologram does not allow a reliable cell count when the concentration increases, and/or when the particles are in motion. The method also shows limits when the hologram has a low noise ratio, for example when the size of a particle is small or when a particle has a refractive index close to that of the medium forming the sample. This document also describes a determination of a three-dimensional position of particles by combining different holograms. This document also describes an application of lensless imaging to tracking the movement, or “tracking” of particles, in particular in a chemotaxis application, the particles being mobile relative to a medium in which they are immersed.

The patent application FR3031180 describes a determination of a three-dimensional position of particles, by applying a digital focusing algorithm, to an image obtained by defocused imaging.

The patent application US2012/148141 describes a process incorporating the principles of WO2008090330 , by implementing a holographic reconstruction algorithm to a succession of acquired images to reconstruct complex images of spermatozoa. The objective is to characterize their motility. This is a process based on individual tracking of mobile particle trajectories, in a motionless fluid, on the basis of three-dimensional coordinates of the particles, obtained by holographic reconstruction. Indeed, such a reconstruction makes it possible to provide an estimate of a distance between an image sensor and a particle, allowing access to so-called depth information, supplementing the two-dimensional information obtained by conventional image sensors. . Furthermore, the method makes it possible to determine a displacement model of each particle, the displacement model being a result obtained by the implementation of the method.

Moreover, many flow imaging methods, designated by the English acronym "Particle Imaging Velocimetry", implement methods for the optical detection of particles so as to characterize their movement, which is representative of the movement of the fluid studied.

The inventors of the present invention have proposed a method for locating and counting particles circulating in a fluidic chamber, which can be automated and implemented in a moving fluid. The method can be implemented automatically, and address high particle velocities or quantities. Additionally, when particles of different types are present in the fluidic chamber, the process can allow discrimination between different types of particles, so as to count the number of particles of different types, based on their respective displacements.

DISCLOSURE OF THE INVENTION

1. a) disposition de la chambre fluidique entre une source de lumière et un capteur d'image, le capteur d'image s'étendant selon un plan de détection ;
2. b) illumination de la chambre fluidique par la source de lumière, la source de lumière émettant une onde lumineuse incidente se propageant selon un axe de propagation et acquisition, par le capteur d'image, d'une image, dite première image, représentative d'une onde lumineuse, dite onde d'exposition, à laquelle est exposé le capteur d'image, le capteur d'image comportant différents pixels, à chaque pixel étant associé une coordonnée radiale dans le plan de détection ;
3. c) à partir de l'image acquise, obtention de coordonnées, notamment tridimensionnelles, de particules, dans la chambre fluidique, à un premier instant,
4. d) obtention de coordonnées, notamment tridimensionnelles, de particules dans la chambre fluidique à un deuxième instant, postérieur au premier instant ;
5. e) à partir des coordonnées des particules obtenues au premier instant et au deuxième instant, détermination de déplacements potentiels des particules entre lesdits instants.
6. f) prise en compte d'un modèle de déplacement du fluide dans la chambre fluidique ;
7. g) à partir du modèle de déplacement du fluide considéré lors de l'étape f), validation de déplacements parmi les déplacements potentiels calculés lors de l'étape e) ;
8. h) à partir des déplacements validés lors de l'étape g), détermination d'un nombre de particules et/ou des coordonnées des particules au premier instant et/ou au deuxième instant.

An object of the invention is a method for counting moving particles in a fluid, circulating in a fluidic chamber, the method comprising the following steps:

1. a) arrangement of the fluidic chamber between a light source and an image sensor, the image sensor extending along a detection plane;
2. b) illumination of the fluidic chamber by the light source, the light source emitting an incident light wave propagating along an axis of propagation and acquisition, by the image sensor, of an image, called the first image, representative of a light wave, called exposure wave, to which the image sensor is exposed, the image sensor comprising different pixels, each pixel being associated with a radial coordinate in the detection plane;
3. c) from the acquired image, obtaining coordinates, in particular three-dimensional, of particles, in the fluidic chamber, at a first instant,
4. d) obtaining coordinates, in particular three-dimensional, of particles in the fluidic chamber at a second instant, subsequent to the first instant;
5. e) from the coordinates of the particles obtained at the first instant and at the second instant, determination of potential displacements of the particles between said instants.
6. f) taking into account a model of movement of the fluid in the fluidic chamber;
7. g) from the displacement model of the fluid considered during step f), validation of displacements among the potential displacements calculated during step e);
8. h) from the displacements validated during step g), determination of a number of particles and/or of the coordinates of the particles at the first instant and/or at the second instant.

• l'obtention d'une première image d'intérêt à partir de la première image acquise lors de l'étape b), et l'application d'un opérateur de propagation numérique à première image d'intérêt, selon au moins une distance de reconstruction, le long de l'axe de propagation, de façon à obtenir au moins une image complexe reconstruite ;
• à partir de chaque image complexe reconstruite, l'obtention de coordonnées radiales de particules dans la chambre fluidique au premier instant.

Step c) may include:

• obtaining a first image of interest from the first image acquired during step b), and applying a digital propagation operator to the first image of interest, according to at least a distance of reconstruction, along the propagation axis, so as to obtain at least one reconstructed complex image;
• from each reconstructed complex image, obtaining radial coordinates of particles in the fluidic chamber at the first instant.

• ci) obtention d'une première image d'intérêt à partir de la première image acquise lors de l'étape b), et application d'un opérateur de propagation numérique à la première image d'intérêt, selon une pluralité de distances de reconstruction, selon l'axe de propagation, de façon à obtenir une première pile d'images complexes reconstruites, dite première pile d'images, comportant autant d'images complexes reconstruites que de distances de reconstruction, chaque image complexe reconstruite étant représentative d'une onde lumineuse d'exposition à laquelle est exposé le capteur d'image;
• cii) pour au moins une coordonnée radiale définie par la première image d'intérêt, détermination d'une distance de reconstruction maximisant l'évolution d'une composante de chaque image complexe formant la première pile d'images, le long d'un axe parallèle à l'axe de propagation et passant par ladite coordonnée radiale, ladite distance de reconstruction déterminée formant une coordonnée transversale associée à ladite coordonnée radiale, la valeur de la composante calculée à ladite distance de reconstruction étant une valeur dite maximale associée à ladite coordonnée radiale, la sous-étape cii) étant réalisée pour tout ou partie de coordonnées radiales associées aux pixels de la première image d'intérêt;
• ciii) établissement d'une liste de positions tridimensionnelles, chaque position tridimensionnelle comportant une coordonnée radiale et la coordonnée transversale associée, déterminée lors de la sous-étape cii), à chaque position tridimensionnelle étant associée la valeur maximale obtenue lors de la sous-étape cii) ;
• civ) sélection de positions tridimensionnelles en fonction de la valeur maximale qui leur est associée.

The particles occupying different transverse coordinates along the axis of propagation, Z, step c) may include the following sub-steps:

• ci) obtaining a first image of interest from the first image acquired during step b), and application of a digital propagation operator to the first image of interest, according to a plurality of reconstruction distances , along the propagation axis, so as to obtain a first stack of reconstructed complex images, called the first stack of images, comprising as many reconstructed complex images as there are reconstruction distances, each reconstructed complex image being representative of a exposure light wave to which the image sensor is exposed;
• cii) for at least one radial coordinate defined by the first image of interest, determination of a reconstruction distance maximizing the evolution of a component of each complex image forming the first stack of images, along an axis parallel to the axis of propagation and passing through said radial coordinate, said determined reconstruction distance forming a transverse coordinate associated with said radial coordinate, the value of the component calculated at said reconstruction distance being a so-called maximum value associated with said radial coordinate , sub-step cii) being carried out for all or part of the radial coordinates associated with the pixels of the first image of interest;
• ciii) establishment of a list of three-dimensional positions, each three-dimensional position comprising a radial coordinate and the associated transverse coordinate, determined during sub-step cii), with each three-dimensional position being associated the maximum value obtained during sub-step cii);
• civ) selection of three-dimensional positions according to the maximum value associated with them.

• la première image acquise lors de l'étape b) ;
• ou la première image acquise lors de l'étape b), à laquelle est soustraite une image de la chambre fluidique, acquise par le capteur d'image, antérieurement ou postérieurement à l'acquisition de la première image, la soustraction étant pondérée par un terme de pondération, le terme de pondération pouvant être un réel compris entre 0 et 1 ;
• ou la première image acquise lors de l'étape b), à laquelle est soustraite une moyenne d'images acquises respectivement antérieurement et postérieurement à l'acquisition de la première image.

The first image of interest can be:

• the first image acquired during step b);
• or the first image acquired during step b), from which is subtracted an image of the fluidic chamber, acquired by the image sensor, previously or after the acquisition of the first image, the subtraction being weighted by a weighting term, the weighting term possibly being a real between 0 and 1;
• or the first image acquired during step b), from which is subtracted an average of images acquired respectively before and after the acquisition of the first image.

The component considered during sub-step cii) can comprise the real part, or the imaginary part, or the modulus, or the phase of each complex image forming the stack of images.

• une formation d'une image, dite première image des maximas, dont chaque pixel est associé à une position tridimensionnelle déterminé lors de la sous-étape ciii), et est affecté de la valeur maximale déterminée, lors de la sous-étape ciii), pour ladite position tridimensionnelle ;
• une sélection, dans la première image des maximas, de pixels dont la valeur est maximale dans une zone de voisinage définie autour de chaque pixel ;
• un calcul, pour chaque pixel sélectionné, d'un rapport signal sur bruit en fonction de ladite valeur maximale et de la valeur de pixels de la première image des maximas situés dans une zone de calcul s'étendant autour dudit pixel ;

de telle sorte que chaque position tridimensionnelle est sélectionnée en fonction du rapport signal à bruit calculé pour le pixel de la première image des maximas qui lui est associé. L'étape de sélection de pixels est facultative, mais préférable, et le rapport signal sur bruit peut être calculé sur tous les pixels de l'image des maximas. Une position dont le pixel associé, sur la première image des maximas, n'est pas sélectionné, est invalidée.Sub-step civ) may include:

• formation of an image, called the first image of the maxima, each pixel of which is associated with a three-dimensional position determined during sub-step ciii), and is assigned the maximum value determined, during sub-step ciii), for said three-dimensional position;
• a selection, in the first image of the maxima, of pixels whose value is maximum in a neighborhood zone defined around each pixel;
• a calculation, for each selected pixel, of a signal-to-noise ratio as a function of said maximum value and of the value of pixels of the first image of the maxima located in a calculation zone extending around said pixel;

such that each three-dimensional position is selected as a function of the signal-to-noise ratio calculated for the pixel of the first image of the maxima which is associated with it. The pixel selection step is optional, but preferable, and the signal to noise ratio can be calculated on all the pixels of the image of the maxima. A position whose associated pixel, on the first image of the maxima, is not selected, is invalidated.

• di) obtention d'une deuxième image d'intérêt à partir de la deuxième image acquise et application d'un opérateur de propagation numérique à la deuxième image d'intérêt, selon une pluralité de distances de reconstruction, selon l'axe de propagation, de façon à obtenir une deuxième pile d'images complexes reconstruites, dite deuxième pile d'images, comportant autant d'images complexes reconstruites que de distances de reconstruction, chaque image complexe reconstruite étant représentative d'une onde lumineuse d'exposition à laquelle est exposé le capteur d'image au deuxième instant ;
• dii) pour au moins une coordonnée radiale définie par la deuxième image d'intérêt, détermination d'une distance de reconstruction maximisant l'évolution d'une composante de chaque image complexe formant la deuxième pile d'images, le long d'un axe parallèle à l'axe de propagation et passant par ladite coordonnée radiale, ladite distance de reconstruction formant une coordonnée transversale associée à ladite coordonnée radiale, la valeur de la composante calculée à ladite distance de reconstruction étant une valeur dite maximale associée à ladite coordonnée radiale, la sous étape dii) étant réalisée pour tout ou partie de coordonnées radiales associées aux pixels de la deuxième image d'intérêt;
• diii) établissement d'une liste de positions tridimensionnelles, chaque position tridimensionnelle comportant une coordonnée radiale et la coordonnée transversale associée, déterminée lors de la sous-étape dii), à chaque position tridimensionnelle étant associée la valeur maximale obtenue lors de la sous-étape dii) ;
• div) sélection de positions tridimensionnelles en fonction de la valeur maximale qui leur est associée.

According to one embodiment, step d) may include acquisition, by the image sensor, of a second image, each pixel of which is associated with a radial coordinate in the detection plane ( P ₀ ). According to this embodiment, step d) comprises the following sub-steps:

• di) obtaining a second image of interest from the second acquired image and application of a digital propagation operator to the second image of interest, according to a plurality of reconstruction distances, along the axis of propagation, so as to obtain a second stack of reconstructed complex images, called the second stack of images, comprising as many reconstructed complex images as there are reconstruction distances, each reconstructed complex image being representative of an exposure light wave at which exposed the image sensor at the second time;
• dii) for at least one radial coordinate defined by the second image of interest, determination of a reconstruction distance maximizing the evolution of a component of each complex image forming the second stack of images, along an axis parallel to the axis of propagation and passing through said radial coordinate, said reconstruction distance forming a transverse coordinate associated with said radial coordinate, the value of the component calculated at said reconstruction distance being a so-called maximum value associated with said radial coordinate, the sub-step dii) being carried out for all or part of the radial coordinates associated with the pixels of the second image of interest;
• diii) establishment of a list of three-dimensional positions, each three-dimensional position comprising a radial coordinate and the associated transverse coordinate, determined during sub-step dii), with each three-dimensional position being associated the maximum value obtained during sub-step dii);
• div) selection of three-dimensional positions according to the maximum value associated with them.

• la deuxième image acquise ;
• ou la deuxième image acquise, à laquelle est soustraite une image de la chambre fluidique, acquise par le capteur d'image, antérieurement ou postérieurement à l'acquisition de la deuxième image, la soustraction étant pondérée par un terme de pondération ;
• ou la deuxième image acquise, à laquelle est soustraite une moyenne d'images acquises respectivement antérieurement et postérieurement à l'acquisition de la deuxième image.

During sub-step di), the second image of interest can be:

• the second acquired image;
• or the second acquired image, from which is subtracted an image of the fluidic chamber, acquired by the image sensor, before or after the acquisition of the second image, the subtraction being weighted by a weighting term;
• or the second image acquired, from which is subtracted an average of images acquired respectively before and after the acquisition of the second image.

During sub-step dii), the component may comprise the real part, or the imaginary part, or the modulus, or the phase of each complex image forming the stack of images.

• une formation d'une image, dite deuxième image des maximas, dont chaque pixel est associé à une position tridimensionnelle déterminé lors de la sous-étape diii), et est affecté de la valeur maximale déterminée, lors de la sous-étape diii), pour ladite position tridimensionnelle ;
• une sélection, dans la deuxième image des maximas, de pixels dont la valeur est maximale dans une zone de voisinage définie autour de chaque pixel ;
• un calcul, pour chaque pixel sélectionné, d'un rapport signal sur bruit en fonction de ladite valeur maximale et de la valeur de pixels de la deuxième image des maximas situés dans une zone de calcul s'étendant autour dudit pixel ;

de telle sorte que chaque position tridimensionnelle est sélectionnée en fonction du rapport signal à bruit calculé pour le pixel de la deuxième image des maximas qui lui est associé. L'étape de sélection est facultative, mais préférable, et le rapport signal sur bruit peut être calculé sur tous les pixels de l'image des maximas. Une position dont le pixel associé, sur la deuxième image des maximas, n'est pas sélectionné, est invalidée.The div) sub-step can have:

• formation of an image, called the second image of the maxima, each pixel of which is associated with a three-dimensional position determined during sub-step diii), and is assigned the maximum value determined, during sub-step diii), for said three-dimensional position;
• a selection, in the second image of the maxima, of pixels whose value is maximum in a neighborhood zone defined around each pixel;
• a calculation, for each selected pixel, of a signal-to-noise ratio as a function of said maximum value and of the value of pixels of the second image of the maxima located in a calculation zone extending around said pixel;

such that each three-dimensional position is selected as a function of the signal-to-noise ratio calculated for the pixel of the second image of the maxima which is associated with it. The selection step is optional, but preferable, and the signal to noise ratio can be calculated on all the pixels of the image of the maxima. A position whose associated pixel, on the second image of the maxima, is not selected, is invalidated.

• l'étape b) comporte deux illuminations successives de la chambre fluidique par la source de lumière, au premier instant et au deuxième instant, de telle sorte que la première image (I) représente l'onde d'exposition (14) à chacun des instants ;
• les étapes c) et d) sont confondues en une même étape d'obtention des coordonnées de particules au premier instant et au deuxième instant.

According to one embodiment:

• step b) comprises two successive illuminations of the fluidic chamber by the light source, at the first instant and at the second instant, such that the first image ( I ) represents the exposure wave (14) at each of the moments;
• steps c) and d) are combined in the same step of obtaining the coordinates of particles at the first instant and at the second instant.

• l'étape e) peut comporter une comparaison des coordonnées des particules dans la chambre fluidique déterminées au premier instant et au deuxième instant, de manière à établir une liste de déplacements potentiels des particules entre lesdits instants.
• l'étape g) comporte une détermination d'une plage de déplacements à l'aide du modèle de déplacement pris en compte lors de l'étape f), les déplacements potentiels étant validés lorsqu'ils sont compris dans ladite plage de déplacement.
• l'étape g) comporte une soustraction, à chaque déplacement potentiel, d'un déplacement selon le modèle de déplacement.

The process may include one of the characteristics taken in isolation or according to technically feasible combinations:

• step e) may include a comparison of the coordinates of the particles in the fluidic chamber determined at the first instant and at the second instant, so as to establish a list of potential displacements of the particles between said instants.
• step g) comprises a determination of a range of displacements using the displacement model taken into account during step f), the potential displacements being validated when they are included in said displacement range.
• step g) comprises a subtraction, at each potential displacement, of a displacement according to the displacement model.

According to one embodiment, the fluid comprises particles, each particle presenting and moving, relative to the fluid, according to a movement model, called a particle movement model, depending on said property. According to this embodiment, the method comprises, from displacements validated during step g), a step i) of taking into account at least one model of particle displacement, so as to count the particles as a function of a value of said property. According to a variant, the method comprises, from potential displacements determined during step e), a step e′) of taking into account at least one model of particle displacement, so as to count the particles according to of a value of said property. The property is a mass or an electric charge, or an ability to move in the fluid.

• une prise en compte d'un modèle de déplacement particulaire pour une valeur prédéterminée de la propriété ;
• un calcul d'écarts des déplacements de chaque particule par rapport au modèle de déplacement particulaire ;

de telle sorte que la propriété de chaque particule est déterminée en fonction desdits écarts ainsi que de ladite valeur prédéterminée de la propriété.According to this embodiment, the method may comprise:

• taking into account a model of particle displacement for a predetermined value of the property;
• a calculation of deviations of the displacements of each particle with respect to the particle displacement model;

such that the property of each particle is determined based on said deviations as well as said predetermined value of the property.

The fluid can flow in one direction of flow, and the particle displacement can take place in another direction that is not parallel to said direction of flow.

The method may be such that no imaging optics are disposed between the image sensor and the fluidic chamber. It may also be such that the image sensor comprises image forming optics between the image sensor and the fluidic chamber, the image formed during step b) being a defocused image.

• une source de lumière configurée pour illuminer la chambre fluidique ;
• un capteur d'image s'étendant dans un plan de détection, la chambre fluidique étant interposée entre le capteur d'image et la source de lumière, le capteur d'image étant configuré pour acquérir une pluralité d'images de la chambre fluidique illuminée par la source de lumière, à un instant ou à des instants successifs ;
• le dispositif comportant un processeur apte à mettre en oeuvre les étapes c) à e) ou c) à h) d'un procédé tel que décrit dans cette demande, à partir d'au moins une image acquise par le capteur d'image.

Another object of the invention is a device for counting particles circulating in a fluidic chamber, the device comprising:

• a light source configured to illuminate the fluidic chamber;
• an image sensor extending in a detection plane, the fluid chamber being interposed between the image sensor and the light source, the image sensor being configured to acquire a plurality of images of the illuminated fluid chamber by the light source, at one instant or at successive instants;
• the device comprising a processor able to implement steps c) to e) or c) to h) of a method as described in this application, from at least one image acquired by the image sensor.

Other advantages and characteristics will emerge more clearly from the following description of particular embodiments of the invention, given by way of non-limiting examples, and represented in the figures listed below.

FIGURES

• There Figure 1A represents an example of a device allowing the implementation of the invention. There figure 1B is a detail of Figure 1A .
• There figure 2A illustrates a succession of steps allowing implementation of a first embodiment of the method. THE figures 2B, 2C and 2D show profiles of part of an acquired image extending around a hologram corresponding to a particle, at three successive instants. There figure 2E shows a profile corresponding to a fixed component, obtained by combining the profiles of the figures 2B to 2D . There figure 2F shows a profile corresponding to a mobile component, this profile being obtained by subtracting the fixed component, represented on the 2D figure , in the profile of the Fig. 2C . There figure 2G represents a profile modeling the evolution of the modulus of a complex image reconstructed from an image corresponding to the profile represented on the figure 2F . There figure 2H represents a profile modeling the evolution of the opposite of the real part of a complex image reconstructed from an image corresponding to the profile represented on the figure 2F .
• There Figure 3A shows a fluidic chamber implemented during a first experimental test. There Figure 3B is a plot of results obtained during the first experimental run. There Fig. 3C shows the impact of a threshold value on counting performance.
• There figure 4A illustrates a succession of steps allowing implementation of a second embodiment of the method. There figure 4B shows results obtained from a second experimental run.
• There figure 5 shows results obtained from a third experimental test, implementing a variant of the invention.

DESCRIPTION OF PARTICULAR EMBODIMENTS

There Figure 1A represents an example of a device allowing an implementation of the invention. A light source 11 is capable of emitting a light wave 12, called an incident light wave, propagating towards a sample 10, along a propagation axis Z. The light wave is emitted along a spectral band Δλ.

The sample 10 is a sample comprising particles 10a which it is desired to count, the particles being placed in a transparent or translucent carrier fluid medium 10b.

The particles are small-sized elements, and are inscribed in a diameter between 0.1 µm and 100 µm; or between 1 µm and 100 µm. The particles are solid or liquid. It may be dust, or cells or microorganisms or microbeads, usually used in biological applications, or even microalgae. They may also be droplets 10b that are insoluble in the fluid, for example droplets of oil dispersed in an aqueous phase. The carrier medium 10b is a fluid, for example air or a liquid, for example water or a biological liquid. The sample may for example be an aerosol, comprising particles in suspension in a gas, the latter possibly being in particular air.

The sample 10 is contained in a fluidic chamber 15. The thickness e of the sample 10, along the axis of propagation typically varies between 10 μm and 2 cm or 3 cm, and is preferably between 20 μm and 1 cm. The sample extends along a plane, called the plane of the sample, preferably perpendicular to the axis of propagation Z. The fluidic chamber 15 is held on a support 10s facing the image sensor 20.

The particles 10a are mobile in the fluidic chamber 15, being carried by the fluid 10b, the latter being mobile in the fluidic chamber 15. In this example, the fluid flows, in the fluidic chamber 15, along an axis of longitudinal flow X. The particles 10a are then driven by the fluidic movement of the medium 10b, the latter acting as a carrier medium, and forming a fluidic current inside the fluidic chamber 15. The displacement of the medium can be modeled. The particles 10a can also be mobile with respect to the medium 10b, the movement of the particles with respect to the fluid which carries them being designated by the term particle movement. Thus, the movement of the particles 10a in the fluidic chamber 15 is not random and obeys a predetermined movement pattern, combining the fluidic movement of the medium 10b and, possibly, the particle movement of the particles with respect to the fluid.

The distance D between the light source 11 and the sample 10 is preferably greater than 1 cm. It is preferably between 2 and 30 cm. Advantageously, the light source, seen by the sample, is considered to be point-like. This means that its diameter (or its diagonal) is preferably less than one tenth, better still one hundredth of the distance between the sample and the light source. In the example represented, the light source 11 is a laser diode. According to a variant, the light source 11 is a white light source or a light-emitting diode. In this case, a spatial filter is advantageously arranged between the light source and the sample, so that the light source appears as a point. The spatial filter can be a pinhole or an optical fiber. A wavelength filter is also preferably placed between the light source and the sample, to adjust the spectral emission band Δλ of the incident light wave 12. Preferably, the spectral emission band Δλ of the incident light wave 12 has a width of less than 100 nm. By spectral bandwidth is meant a width at mid-height of said spectral band.

The fluidic chamber 15 is placed between the light source 11 and the image sensor 20 mentioned above. The latter preferably extends parallel, or substantially parallel to the plane along which the sample extends. The term substantially parallel means that the two elements may not be strictly parallel, an angular tolerance of a few degrees, less than 20° or 10° being allowed. The image sensor 20 is able to form an image I according to a detection plane P ₀ . As shown on the figure 1B , the image sensor comprises a matrix of pixels, each pixel being associated with coordinates (x,y), called radial coordinates, in the detection plane P ₀ . The image sensor can in particular be a CCD or CMOS type sensor. The detection plane P ₀ preferably extends perpendicular to the axis of propagation Z of the incident light wave 12. The distance between the sample 10 and the matrix of pixels of the image sensor 20 is between a distance d _min and a maximum distance d _max . The thickness e of the fluidic chamber corresponds to the difference between d _max and d _min . d _min can be between 50 μm and 2 cm, preferably between 100 μm and 2 mm. The thickness of the fluidic chamber is generally between 100 μm and 5 cm.

Note the absence of an optical image forming system, in particular of magnification optics between the image sensor 20 and the sample 10. This does not prevent the possible presence of focusing microlenses at the level of each pixel of the image sensor 20, the latter having no magnification function of the image acquired by the image sensor. The image sensor 20 is thus placed according to a so-called lensless imaging configuration. Such a configuration makes it possible to obtain a high field of view. Other configurations are nevertheless possible, in particular a configuration according to which a focusing optic is interposed between the image sensor 20 and the fluidic chamber 15. In such a configuration, the image sensor acquires a defocused image of the sample 10, as described in EP3031180 .

• une composante 13 résultant de la diffraction de l'onde lumineuse incidente 12 par chaque particule de l'échantillon ;
• une composante 12' résultant de l'absorption de l'onde lumineuse incidente 12 par l'échantillon.

Under the effect of the incident light wave 12, the particles present in the fluidic chamber 15 can generate a diffracted wave 13, capable of producing, at the level of the detection plane P ₀ , interference with part of the light wave incident 12 transmitted by the sample. Furthermore, the sample can absorb part of the incident light wave 12. Thus, the light wave 14, called exposure light wave, transmitted by the sample 10 and to which the image sensor 20 is exposed , may include:

• a component 13 resulting from the diffraction of the incident light wave 12 by each particle of the sample;
• a component 12' resulting from the absorption of the incident light wave 12 by the sample.

These components form interferences in the detection plane. Also, the image I acquired by the image sensor 20 comprises interference figures (or diffraction figures), each interference figure being generated by a particle 10a of the sample 10.

A processor 30, for example a microprocessor, is configured to process each image I acquired by the image sensor 20. In particular, the processor is a microprocessor connected to a programmable memory 32 in which is stored a sequence of instructions for carrying out the image processing and calculation operations described in this description. The processor can be coupled to a screen 34 allowing the display of images acquired by the image sensor 20 or calculated by the processor 30.

The fluidic chamber 15 is fixed relative to the image sensor 20. Thus, the fluidic medium 10b and the particles 10a circulating in the fluidic chamber are in motion relative to the image sensor 20.

As indicated in relation to the prior art, it is possible to apply, to each image acquired by the image sensor, a propagation operator h, so as to calculate a complex quantity representative of the exposure light wave 14. It is then possible to calculate a complex expression A of the light wave 14 at any point of coordinates ( x,y,z ) in space, and in particular along a reconstruction surface extending opposite the image sensor 20 The reconstruction surface is usually a plane P _z , called reconstruction plane, extending parallel to the image sensor 20, at a coordinate z of the detection plane P ₀ . The reconstruction plane P _z is then parallel to the detection plane P ₀ . One then obtains an image, called complex image A _z , representative of the exposure light wave 14 in the plane reconstruction P _z . The complex image A _z is obtained by a convolution of the image I acquired by the image sensor 20 by the propagation operator h, according to the expression: A _z = I ∗ h.

The propagation operator h describes the propagation of light between the detection plane P ₀ and the reconstruction plane P _z . In this example, the detection plane P ₀ has the equation z = 0.

The propagation operator is for example a so-called Fresnel operator, defined according to the following expression: $$h_z\left(x,\mathrm{there}\right)=\frac{-I}{\mathit{\lambda z}}{e}^{\frac{\mathit{i\pi }}{\mathit{\lambda z}}\left(x^2+{\mathrm{there}}^2\right)}$$

.A feature of the invention is that the particles 10a move, being driven by the fluid 10b. The fluid moves between an inlet and an outlet of the fluidic chamber 15, along a flow axis X. In order to count them, it is necessary to obtain three-dimensional positions of the particles at a first instant t ₁ and at a second instant t ₂ , subsequent to the first instant, the time shift Δ t = t ₂ - t ₁ between the two instants depending on a maximum speed V _max of the fluid in the fluidic chamber 15 as well as on the dimension of the part of the fluidic chamber seen by the sensor. If L designates a dimension of the fluidic chamber 15, seen by the image sensor 20, along the propagation axis X of the fluid, it is preferable that: $\mathit{\Delta t}<\frac{I}{2}{V}_{\mathit{max}}$

.

Different embodiments are possible. According to a first embodiment, the image sensor acquires two successive images I ( t ₁ ) and I ( t ₂ ) , respectively at the first instant t ₁ and at the second instant t ₂ . From each image, three-dimensional coordinates of the particles are obtained at each instant. According to a second embodiment, the same image of the fluidic chamber is acquired at the two instants, the acquisition of this image being carried out at the first instant and at the second instant.

The main steps of the first embodiment of the method are described below, in connection with the figure 2A .

Step 100 : acquisition. This involves acquiring an image I ( t _i ) at different instants t _i , according to an acquisition frequency. During a first iteration, the instant t _i is a first instant t ₁ and an image called the first image I ( t ₁ ) is acquired. During a second iteration, time t _i is a second time t ₂ , the second time being later than the first time. The image acquired at time t ₂ is a second image I(t ₂ ).

Step 110 : extraction of an image of interest from the acquired image, the image of interest representing a moving component I _m ( t _i ) of the acquired image. The acquired image I ( t _i ) comprises a component I _f ( t _i ) , called fixed component, representing the elements considered as not dependent on time, and a component I _m ( t _i ) called moving component, representing the elements considered like moving in the picture. The particles moving in the sample are in motion and form the motion component. The first filtering aims to remove the fixed component of the acquired image. The fixed component can be obtained by means of one or more images acquired at different instants different from the instant of acquisition of the filtered image. The fixed component I _f (t _i ) can be estimated by an initial image I(t ₀ ), acquired when no particle is circulating in the fluidic chamber 15. This makes it possible to obtain an image of the fixed elements, for example dust, not representative of the mobile particles to be counted. Preferably, the fixed component I _f (t _i ) is estimated by an average between an image acquired at a time prior to and an image acquired at a time subsequent to the acquisition time t _i of the acquired image. It may be for example the previous instant t _{i -1} and the following instant t _{i +1} the instant of acquisition t _i , in which case the fixed component is such that $$I_f\left({\mathrm{you}}_I\right)=\frac{I\left({\mathrm{you}}_{I+1}\right)+I\left({\mathrm{you}}_{I-1}\right)}{2}$$

The estimation of the fixed component is thus renewed on each new acquisition of an image. It corresponds to an average of two images respectively acquired before and after the acquired image considered, the average being weighted by a weighting factor of ½. This allows regular updating of the fixed component.

The fixed component is subtracted from each acquired image, so as to obtain a mobile component I _v , representative of the mobile elements in the image, and in particular of the mobile particles. I _v ( t _i ) = I(t _i ) - I _f (i) (3).

The moving component forms an image of interest based on which the following steps are performed. During the first instant t ₁ , the image of interest is denoted I _v ( t ₁ ). During the second instant t ₂ , the image of interest is denoted I _v (t ₂ ).

THE figures 2B to 2D represent modeled examples of intensity profiles of a hologram, corresponding to a particle, on an image acquired by the image sensor 20, respectively at times t _{i -1} , t _i , and t _{i +1} . The particle moves along the flow axis of the fluid X, which results in a translation of the hologram, the latter being represented by a bracket on each of these figures. The ripples observed on either side of the hologram correspond to the effect of assembly imperfections. These imperfections are in particular non-uniformities of illumination and interference between reflections taking place at the interfaces of the chamber. This results in the fact that on the figures 2B, 2C and 2D , the profiles at the level of the holograms are asymmetrical and different. There figure 2E shows the fixed component, I _f (t _i ) as determined by expression (2). The fixed component I _f (t _i ) comprises the effect of the imperfections of the assembly as well as the holograms corresponding to the instants t _{i -1} and t _{i +1} , the latter being weighted by a weighting factor equal to ½. There figure 2F represents the mobile component I _v ( t _i ) obtained according to expression (3). It is observed that the effect of imperfections has disappeared. The central hologram, corresponding to the position of the particle at time t _i , is symmetrical. The holograms corresponding to the instants t _{i -1} and t _{i +1} are also symmetrical and are weighted with a weighting factor equal to -1/2. These residual holograms are called echoes.

Thus, this step makes it possible to estimate a moving component I _v ( t _i ) of the acquired image, this moving component being representative of the moving elements, relative to the image sensor, at the acquisition instant t _i . This mobile component I _v ( t _i ) makes it possible to better show the mobile particles which it is desired to count.

représentant une dimension d'un pixel (longueur ou largeur). Step 120: frequency filtering. The image of interest I _v ( t _i ), resulting from step 110, is subject to band-pass frequency filtering: such filtering makes it possible to eliminate low spatial frequencies, linked to heterogeneities of the illumination of the sample, as well as high spatial frequencies, the latter being considered as noise. The bandwidth of the frequency filter is preferably between a low cutoff frequency and a high cutoff frequency. The low cutoff frequency can be equal to 0.02 f. The high cutoff frequency can be equal to 0.5 f. f is a frequency corresponding to half the spatial frequency defined by the size of the pixels: $f=\frac{1}{2\mathcal{I}}.\mathcal{I}$

representing a dimension of a pixel (length or width).

Step 130 : propagation of the filtered image. The image resulting from step 120 is propagated along different reconstruction distances z _j , along the propagation axis Z. The reconstruction distances are determined such that the reconstruction planes P _{z I} respectively associated with each reconstruction distance z _j are included in the sample. Thus, from each acquired image I ( t _i ), after the steps of extracting the image of interest 120 and filtering 130, a stack of complex images A _z is formed _I ( t _i ) reconstructed at different reconstruction distances z _j . If d _min and d _max denote respectively the minimum distance and the maximum distance between the sample and the image sensor, the reconstruction is performed so as to obtain different reconstruction planes between d _min and d _max . The number of reconstruction distances considered conditions the spatial resolution with which the coordinates of the particles are determined, as described below. The interval between two different reconstruction distances can for example be 100 μm. At the end of this step, a stack of complex images is available, each complex image extending parallel to the detection plane, at a coordinate z _j , called the transverse coordinate.

Step 140 : Extraction of a component of each complex image. This involves associating a real number with each pixel of the complex image. Thus, the stack of complex images A _{z I} ( t _i ) is replaced by a stack of images A' _{z I} ( t _i ) of real numbers, each pixel A' _{z I} (t _i ,x,y) of each real image being a component comp ( A' _{z I} ( t _i ,x,y )) of a complex image A _{z I} ( t _i ) , at the transverse coordinate z _j , at the same radial coordinate (x,y) (that is to say at the same pixel). By component of a complex image is meant a quantity obtained from the complex image at the radial coordinate (x, y ). The component can be or include the real part, the imaginary part, or the modulus, or the phase, of the complex amplitude A _{z I} ( x,y ) of the complex image A _{z I} ( t _i ) at the radial position ( x , y ). The component can combine quantities listed in the previous sentence. We seek here to obtain a transverse coordinate z _j , denoted z _xy , maximizing the component, and that for each radial coordinate ( x, y ). This maximization is the subject of step 145.

THE figures 2G and 2H represent respectively a profile of a component of a reconstructed complex image, at a radial coordinate z _j , the complex image being obtained by holographic reconstruction of the image I _v (t _i ) whose profile is represented on the figure 2F . On the figure 2G , the profile of the modulus of the reconstructed complex image has been represented. On the figure 2H , the profile of the opposite of the real part of the reconstructed complex image has been represented. In other words, the figures 2G and 2H represent the profile of an image of real numbers obtained after extracting a component from the reconstructed complex image, the component being respectively the modulus, | _{Az I} ( _x , _y )| , or the opposite of the real part, -Re ( A _{z I} ( x, y )). We observe that when the component is the opposite of the real part, the central hologram, visible on the profile of the figure 2H , is represented by a large and positive value. The holograms located on either side of the central hologram correspond to residues, or echoes, resulting the extraction of the image of the mobile component. Their amplitude is lower than that of the central histogram, and is negative. It will be understood that it will be easier to discriminate the central hologram, corresponding to the particle at time t _i , from the holograms corresponding to a residue (or echo) of the particle at earlier times t _{i -1} and later times t _{i +1} , these holograms not being representative of the particle at time t _i . On the profile of figure 2G , the hologram of the particle at instant t _i results in a peak of positive and high amplitude, while the holograms of the particle at instants prior t _{i -1} and posterior t _{i +1} result in peaks also of positive amplitude, but less high. It is understood that it is more difficult to discriminate, on the basis of the module, the "useful" holograms, that is to say corresponding to a position of the particle, from the echo holograms, corresponding to a position of a particle at an earlier or later time.

The comparison of figures 2G and 2H shows that the fact of considering the real part, or its opposite, has an advantage, in particular compared to classical approaches of holographic reconstruction, according to which the modulus of a complex amplitude is considered. Indeed, unlike a module, a real part of an image is signed, in the sense that it can be positive or negative. It allows discrimination on the basis of a sign, which is not possible when considering modules. The taking into account of the real part appears particularly relevant when it follows arithmetic combinations of images comprising a subtraction of images.

Step 145 : Digital focus. During this step, one searches, for each pixel of the acquired image, that is to say for each radial position (x,y), a transverse coordinate z , according to the axis of propagation Z for which the component comp ( A _{z I} ( t _i )) of a complex image A _{z I} of the image stack, at pixel coordinates ( x, y ), is maximal. This is to apply a principle known as digital focusing known to those skilled in the art. A particle is present at an unknown distance from the image sensor. The closer the reconstruction distance is to this distance, the more the particle forms, on the reconstructed complex image, a narrow and intense spot. The complex image comprising a real part and an imaginary part, the search for the distance separating the particle from the detector is carried out by analyzing the spatial evolution of a component of each complex image along the axis of propagation.
we determine z _xy , such that

This step is repeated for all or part of the radial positions ( x , y ) of the image sensor so that each radial coordinate (x, y) is associated with a transverse coordinate z _{x, y} as defined in the expression (4).

Step 150: formation of the image of the maxima.

Following step 145, an image called the maxima is formed, such as: $${\mathrm{AT}}_{\mathit{max}}\left(x,\mathrm{there}\right)=\mathit{comp}\left({\mathrm{AT}}_{z_{\mathit{xy}}}\left(x,\mathrm{there}\right)\right)=-\mathit{D}\left({\mathrm{AT}}_{z_{\mathit{xy}}}\left(x,\mathrm{there}\right)\right)$$

This image comprises, at each pixel (x,y), the maximum value of the component, in the stack of complex images A _{z I} , along the axis of propagation Z, determined during step 145. Each pixel (x,y) of the image of the maxima A _max is associated with the transverse coordinate z _xy identified during step 145.

During the first iteration ( t _i = t ₁ ), a first image of the maxima is obtained. During the second iteration ( t _i = t ₂ ), a second image of the maxima is obtained.

Step 160: search for local maxima in the image of the maxima.

During this step, a search for local maximum values is carried out by groups of adjacent pixels. For example, each group of pixels has 51*51 adjacent pixels. A pixel of the image of the maxima A _max is considered as a local maximum if it is the pixel exhibiting the highest value in a group of 51*51 pixels centered on said pixel, forming a neighborhood zone of the pixel. The image of the A _max maxima can be smoothed before the search for local maxima. This may involve smoothing by applying a Gaussian filter or a low-pass filter.

We can thus obtain a list of the coordinates of each local maximum pixel ( x _max , y _max ) as well as the value A _maxi ( x _max , y _max ) of the image of the maxima A _max at this pixel, as well as the coordinate transverse z _{x max there max} associated with this pixel. Other known digital focusing algorithms can be applied to the stack of images resulting from step 140, making it possible to define such a list. Such algorithms can for example be based on a sharpness criterion on each image of the stack of images. These algorithms also make it possible to define local maxima as well as a transverse coordinate associated with these local maxima.

Step 170: consideration of the signal to noise ratio.

The search for local maxima in the image of the A _max maxima can be affected by a non-homogeneous background. This non-homogeneous background is notably caused by fringe fluctuations interference produced by the multiple interfaces between the light source 11 and the image sensor 20. As a result, the inventors have considered that it is preferable to take into account a signal-to-noise ratio at each radial coordinate determined during of step 160. Thus, at each radial position x _max , y _max defined during step 160, a signal-to-noise ratio SNR ( x _max, y _max ) is calculated , this ratio being calculated using the information contained in the image of the maxima A _max . A local noise level is calculated, in the image of the maxima, around each radial position (x _max , y _max ) , for example in a noise calculation zone centered on the position ( x _max ,y _max ) and diameter equal to 200 pixels. The pixels considered for the calculation of the local noise can be all the pixels of the noise calculation zone, or certain pixels of this zone. The inventors have for example taken into account 100 pixels regularly distributed over the circle delimiting the noise calculation zone, the noise level being estimated by calculating the median of the value of these 100 pixels.

This step makes it possible to establish a list of radial coordinates ( x _max , y _max ) corresponding to a local maximum in the image of the maxima, each pair of radial coordinates being associated with a transverse coordinate z _{x max there max} . We then have a list of three-dimensional positions ( x _max , y _max , z _{x max there max} ) in the sample likely to contain a particle. At each three-dimensional position ( x _max ,y _max ,z _{x max there max} ) is associated with an estimate of the signal-to-noise ratio SNR(x _max ,y _max ) of the image A _max at the position ( x _max ,y _max ). Each of these three-dimensional positions is likely to be occupied by a particle 10a at the instant t _i considered.

Step 180 : thresholding. During this step, the three-dimensional positions are subject to thresholding of the signal-to-noise ratio which is respectively assigned to them. The thresholding is carried out according to a threshold value S which can be predetermined. Only the three-dimensional positions whose associated signal-to-noise ratio is greater than the threshold value are retained, the others being considered as not representative of particles. The threshold can be predetermined, for example on the basis of calibrations, or optimized as described later, in connection with step 250.

Step 190: Reiteration. Steps 110 to 180 are repeated on the basis of an image I ( t ₂ ) acquired at the second instant t ₂ . This makes it possible to obtain a list of three-dimensional positions (x _max , y _max , z _{x max there max} ) at time t ₂ , as well as a signal-to-noise ratio associated with each position. Thus, at the end of step 190, a first list of three-dimensional positions ( x _max , y _max , z _{x max there max} )( t ₁ ) at the first time t ₁ and a second list of three-dimensional positions ( x _max ,y _max ,z _{x max there max} )(t ₂ ) at the second instant t ₂ , as well as a signal-to-noise ratio associated with each position.

Step 200 : Calculation of potential displacements. During this step, potential displacements Δ are determined resulting from the comparison between each three-dimensional position at the first instant ( x _max , y _max , z _{x max there max} )( t ₁ ) and at the second instant ( x _max , y _max , z _{x max there max} )( t ₂ ) . This results in a list of potential displacement vectors, the coordinates of which represent potential displacements. There Figure 3B represents displacement vectors whose coordinates are indicated along the X axis (axis of abscissas) and the axis Z (axis of ordinates). Each displacement vector corresponds to a couple comprising a three-dimensional position of a particle ( x _max , y _max , z _{x max there max} )( t ₁ ) , at the first instant, chosen from the first list and another position of a three-dimensional of a particle ( x _max , y _max , z _{x max there max} )( t ₂ ) , at the second instant, chosen from the second list. A first sorting is carried out, on the basis of a minimum displacement and a maximum displacement along each axis, as well as on the basis of a criterion relating to the signal-to-noise ratio allocated to each three-dimensional position: the signal-to-noise ratio SNR(x _max ,y _max ) of the position at the first instant must correspond to the signal-to-noise ratio assigned to the position at the second instant, to within an uncertainty.

Step 210 : Taking into account a displacement model mod. This is based on knowledge of the kinematic parameters of the displacement of the particles 10a in the fluidic chamber 15. For example, the medium 10b in which the particles 10a evolve is in motion in the fluidic chamber 15, the medium 10b carrying the particles. The movement of the medium 10b can be modeled, the particles being considered as following the movement of the medium, at least in one plane. For example, when the fluidic chamber 15 is horizontal, the particles are supposed to follow the model of movement in the horizontal plane, to within a fluctuation corresponding to a movement of the particles in a vertical plane, the latter being due to gravity and depending on the mass of the particles.

Taking into account the displacement model mod makes it possible to define a range of displacement, extending between a first terminal and a second terminal. The displacement range defines the coordinates of the possible displacement vectors given the adopted displacement model. Potential movements outside the movement range are invalidated.

The displacement model can be a parametric model, the parameters of which are adjusted experimentally, based on a statistical processing of the displacements detected over a series of image acquisitions. There Figure 3B represents for example in the form of a cloud of points all the displacements obtained following an analysis of a series of 500 image acquisitions. Each displacement is represented by a circle whose abscissa is the component Δx of the displacement along the longitudinal axis X and whose ordinate is the coordinate of the reconstruction plane z _j corresponding to the position of the particle at the start of the displacement. The points having the same ordinate correspond to displacements whose starting position is located in a plane parallel to the image sensor located at the distance z _j from the latter. Taking into account multiple image acquisitions makes it possible to constitute data relating to the displacement, the statistics of which are sufficient to determine or adjust the parameters of the model. The scatter plot clearly shows a high density area that has a boomerang shape.

At the level of the center of the fluidic chamber 15 (z _j close to 35), the displacements have a maximum amplitude. At the edges of the fluidic chamber 15 (z _j close to 0 or z _j close to 60), the displacements are lower, due to the presence of the walls of the fluidic chamber. Thus, preferably, the displacement model is three-dimensional, so as to take into account a flow velocity distribution of the fluid in a transverse plane YZ perpendicular to the flow axis X of the fluid, in particular because edge effects resulting from the walls of the fluidic chamber 15.

In this example, the boomerang shape is modeled by a degree 3 polynomial. The coefficients of this polynomial can be determined by a quadratic fit to measured data. It is thus possible to determine or refine the parameters of the model, on the basis of the acquired images. Thus, it is based on a parametric displacement model, the parameters of the model being able to be determined or updated by experimental measurements.

On the Figure 3B , a range corresponding to an accepted tolerance with respect to the model has also been shown, which is the zone comprised between the curves M1 and M2.

Step 220 : validation of movements.

During step 220, the potential displacements Δ determined during step 200 are compared to the displacement range defined during step 210. The displacements not included in the displacement range are considered invalid and are eliminated. . THE displacements Δ _v included in the range are validated. In the example of the Figure 3B , the displacement range is defined according to a plane (Δ x, z _j ), in which case the validation is carried out on the basis of a projection of each potential displacement vector according to this plane.

Step 230 : definition of the positions and/or of the number of particles corresponding to valid displacements.

Each movement Δ _v validated during step 220 makes it possible to define a position of a particle at the first instant and a position of a particle at the second instant. A list of positions ( x, y, z)(t ₁ ) validated for particles at the first instant and a list of positions ( x, y, z)(t ₂ ) for particles validated at the second instant are then determined. This list is produced by considering that at the first instant and at the second instant, a particle is associated with only one displacement. Each list thus obtained makes it possible to estimate a position of the particles at the first instant, as well as a position of the particles at the second instant, as well as the number N of particles 10a circulating in the fluidic chamber 15.

Preferably, to validate the position of a particle at a time t _i , 3 different times are considered, for example three successive times t _i-1 , t _i and t _i+1 . The instant t _i represents a so-called current instant, the instants t _{i -1} and t _{i +1} being instants respectively before and after the current instant. From the displacements Δ _v ( t _{i -1} , t _i ) validated between t _{i -1} and t _i , a first list of pairs of positions is established between the instants t _{i -1} and t _i . From displacements Δ _v ( t _i , t _{i +1} ) validated between t _i and t _{i +1} , a second list of pairs of positions is established between the instants t _i and t _{i +1} . The list of particles at the current instant t _i is obtained by performing the union of the first list and the second list, the duplicates being eliminated.

Step 250 : optimization of the threshold

A parameter that may be important for the implementation of the method is the threshold S used during step 180, to select or not the positions of particles. This threshold conditions the number of particles considered to establish the potential displacements. There Fig. 3C represents a change in the number of particles counted, by implementing the method described above, whose signal-to-noise ratio is greater than the value of the abscissa. Such a representation allows a posteriori modification of the threshold, for example by fixing the value of the threshold to an optimal value corresponding to the flattest part of the curve. The inventors consider that an optimal threshold corresponds to the flattest part of the curve, that is to say to a low derivative, the derivative being calculated with respect to the value of the threshold. In the example shown in the Fig. 3C , and described below, the optimal value of the threshold, by implementing the process, is 2.2 or 2.3. It is therefore possible to suppress the particles subsequently having a signal-to-noise ratio below the threshold.

By way of comparison, the figure also shows an evolution of the number of particles N′ counted without considering a displacement, that is to say based only on an image acquired at a given instant. It is observed that taking displacements into account makes it possible to reduce the number of particles counted, in particular when the threshold is low.

During a first experimental test, we used a fluidic chamber, as represented on the Figure 3A , oriented vertically. The test was carried out according to a configuration as described on the figure 1 , the X axis, along which the particles propagate, being vertical and oriented downwards.

• Chambre fluidique : Starna type 45-F : dimensions internes de 5 x 10 x 45 mm.
• Source de lumière : diode laser CiviLaser - 405 nm - durée d'une impulsion 100 µs.
• Capteur d'image : CMOS MIGHTEX BTN-B050-U - 2592 x 1944 pixels de taille 2.2 µm x 2.2 µm.
• Fréquence d'acquisition : 10 Hz.

The sample consists of polystyrene particles with a diameter of 1 μm circulating in an air flow. The experimental parameters are as follows:

• Fluidic chamber: Starna type 45-F: internal dimensions of 5 x 10 x 45 mm.
• Light source: CiviLaser laser diode - 405 nm - pulse duration 100 µs.
• Image sensor: CMOS MIGHTEX BTN-B050-U - 2592 x 1944 pixels of size 2.2 µm x 2.2 µm.
• Acquisition frequency: 10 Hz.

We used 64 reconstruction planes, corresponding to distances, with respect to the image sensor, regularly spaced between 1.5 mm and 7.8 mm.

At each instant, a list of particles is determined, with coordinates ( x, y, z ). Being in a case of weak signals, the detection is made by favoring the detection of a large fraction of the particles with the disadvantage of having many false detections.

From the positions of the particles at two successive instants, the potential displacements Δ are determined, the latter being represented in the form of circles, having a coordinate Δx along the X axis, a coordinate Δ z along the Z axis and a coordinate Δ y along the Y axis. The potential displacements were obtained by taking into account the following sorting criteria: 0 ≤ Δx ≤ 2.2 mm; 0 ≤ Δy ≤ 66 µm ; 0 ≤ Δz ≤ 200 µm .

There Figure 3B illustrates these potential displacements in the form of a cloud of points in the plane (Δ x, Z). A displacement model has been taken into account, forming bounds represented by the curves M1 and M2 drawn on the Figure 3B . The displacements located between these curves have been validated.

From the validated displacements Δv , the number N of particles has been counted, as a function of the signal-to-noise ratio threshold considered during step 180, the evolution of the number N of particles counted as a function of the signal ratio threshold S on noise being represented on the Fig. 3C . This figure also represents a number of particles N ′ as a function of the signal-to-noise ratio threshold, without taking the displacement into account. It can be seen that beyond a certain threshold, the estimate without taking the displacement into account is reliable.

According to a second embodiment, the sample is illuminated by two pulses respectively at a first instant t ₁ and at a second instant t ₂ , and an image I is acquired whose acquisition duration includes the first instant and the second instant . Thus, on the same image, a signal representative of the positions of the particles at the first and at the second instant is obtained. The steps of this embodiment are shown in the figure 4A , and described below:
Step 300: successive illumination of the sample at the first instant and at the second instant, and acquisition of an image I, called the first image, during the first instant and during the second instant. The time interval between the two instants can be very short, for example 5 ms.

Step 320: frequency filtering, analogously to step 120.

Step 330: propagation of the filtered image, analogously to step 130, to obtain a stack of complex images
Step 340: extraction of a component of each complex image from the stack of complex images.

Step 345: digital focusing, analogously to step 145.

Step 350: formation of an image of the maxima from the acquired image, analogously to step 150.

Step 360: search for local maxima in the image of the maxima, analogously to step 160.

Step 370: taking into account the signal-to-noise ratio, analogously to step 170. This step makes it possible to establish a list of radial coordinates ( x _max ,y _max ) corresponding to a local maximum in the image of the maxima , each pair of radial coordinates being associated with a transverse coordinate z _{x max there max} . We then have a list of positions three-dimensional ( x _max ,y _max ,z _{x max there max} ) in the sample likely to contain a particle. At each three-dimensional position (x _max ,y _max , z _{x max there max} ) is associated with an estimate of the signal-to-noise ratio SNR ( x _max ,y _max ) of the image I _max at the position ( x _max ,y _max ). Unlike the first embodiment, each of these three-dimensional positions is capable of being occupied by a particle 10a at the first instant t ₁ or at the second instant t ₂ .

Step 380: thresholding according to a signal-to-noise ratio threshold, analogously to step 180. Only the three-dimensional positions whose associated signal-to-noise ratio is greater than the threshold value are kept, the others being considered as not representative of particles.

Step 400: Calculation of potential displacements. During this step, potential displacements Δ resulting from the comparison between each three-dimensional position obtained following step 380 are determined. This results in a list of potential displacement vectors, the coordinates of which represent potential displacements. There figure 4B represents potential displacements obtained following a second experimental test described below. This step can take into account a sorting criterion, based on a minimum displacement, and therefore a minimum spacing between two positions. Moreover, the sorting criterion can also take into account the fact that the particles move, in one direction, according to a predetermined direction. For example, along the X axis, we consider that Δ xmin ≤ Δx ≤ Δ xmax, with Δ xmin > 0. This takes into account the fact that the three-dimensional positions of the acquired image are likely to correspond to positions of particles at the first instant or at the second instant.

Step 410: taking into account a movement model, analogously to step 210.

Step 420: validation of movements, on the basis of a movement model, as described in connection with step 220. On the figure 4B , a displacement model has been shown (curve M3).

Step 430: definition of the positions and/or of the number of particles corresponding to displacements validated during step 420.

According to this second embodiment, the method may include a step 450 of adjusting the signal-to-noise ratio threshold used, similarly to step 250 previously described.

An advantage of this embodiment is to avoid having recourse to image sensors having an excessively high acquisition frequency. For example, when the time interval between the first instant and the second instant is 5 ms, the first embodiment, based on an acquisition of two successive images, would impose an acquisition rate of 200 images per second, which is not within the reach of usual image sensors. This embodiment is therefore suitable for particles exhibiting high velocities.

This embodiment was the subject of a second experimental test, the particles being polystyrene balls with a diameter of 2 μm moving in the air. There figure 4B represents displacements as well as a modeled border.

, où LX et LY désignent les dimensions du champ observé par le détecteur 20 dans la chambre fluidique 15, respectivement selon l'axe X et l'axe Y.A limitation of this embodiment is that it only takes into account the particles present in the field of observation of the image sensor at the two instants considered. The inventors have estimated that by applying a weighting factor to each displacement detected, the number of particles counted is more reliable. The weighting factor for each displacement Δ _k , is determined according to a probabilistic approach. The probability of detection p _k of coordinates Δ X _k , Δ Y _k is such that: $p_k=\frac{\left(\mathit{LX}-\left|{\mathit{\Delta X}}_k\right|\right)\times \left(\mathit{LY}-\left|{\mathit{\Delta Y}}_k\right|\right)}{\mathit{LX}\times \mathit{LY}}$

, where LX and LY designate the dimensions of the field observed by the detector 20 in the fluidic chamber 15, respectively along the X axis and the Y axis.

If K designates the number of displacements Δ _k validated, each displacement having as coordinates Δ X _k and Δ Y _k , the number of particles in the fluidic chamber can be estimated by: $\mathrm{NOT}={\displaystyle {\sum }_{k=1}^{k=K}\frac{1}{p_k}={\displaystyle {\sum }_{k=1}^{k=K}\frac{\mathit{LX}\times \mathit{LY}}{\left(\mathit{LX}-\left|{\mathit{\Delta X}}_k\right|\right)\times \left(\mathit{LY}-\left|{\mathit{\Delta Y}}_k\right|\right)}}}$

This remains valid only if |Δ X _k | < LX or if |Δ Y _k | < LY

We now describe a variant that can be applied to each embodiment, from the list of potential displacements Δ. This list is obtained at the end of step 200 of the first embodiment or of step 400 of the second embodiment. According to this variant, the particles circulating in the fluidic chamber are of different types, for example of different masses. As a result, each type of particle can exhibit a displacement, called particulate displacement, with respect to the fluid, which is specific to it. Particle displacement can be induced by a property of the particle, conditioning the displacement of the latter relative to the fluid. The particle then moves in the fluid under the effect of a force depending on said property, for example under the effect of a field to which the particle is subjected. It may for example be an electric or magnetic field, in which case a particle is subjected to a force depending on its charge. It can also act from a gravitational field, in which case the particle moves relative to the fluid according to its mass. Thus, it is possible to define a model of particle displacement of particles with respect to the fluid, one parameter of which is said property of the particle. The particle motion of each particle is preferably oriented in an orientation not parallel to the direction of fluid flow, but this condition is not necessary. It is optimal for the particle motion to be perpendicular to the direction of fluid flow. By applying the particle displacement model to the previously validated three-dimensional displacements Δ, it is possible to determine the property of the particle forming a parameter of the particle displacement model. It is then possible to classify the particles according to their property and to count the particles according to a value of said property.

It is for example possible to take into account a model of particle displacement corresponding to a predetermined value of the property. Then, for each particle, a deviation ε with respect to this model is determined. It is then possible to classify the particles according to the deviation ε, with respect to the model of particle displacement, which has been attributed to them. The particles are then classified according to their particle displacement. The particles for which the difference is zero have a property corresponding to the predetermined value. The property of the other particles depends on the difference calculated for each of them.

A third experimental test was carried out to implement this variant, using polystyrene beads with a diameter of 1 μm and a diameter of 2 μm. The fluidic chamber was kept arranged such that the particles were entrained by air flowing horizontally, with the flow axis X being horizontal. The experimental device is represented on the Figure 1A , the XZ plane being a horizontal plane. The particles were dragged horizontally by the carrier fluid, in this case air, along the horizontal axis X. They underwent the effect of gravity, along a vertical axis Y, perpendicular to the flow axis . The dimensions of the fluidic chamber 15 were 10 mm and 20 mm respectively along the Z and Y axes. The fluidic chamber had, in a YZ plane, a rectangular section with dimensions of 9.6 mm×20 mm.

, avec :

• ρ_b : densité du deuxième type de particules;
• d_b : diamètre du deuxième type de particules;
• ρ_a : densité du premier type de particules ;
• d_a : diamètre du premier type de particules;
• K est une constante, égale à 34.7.

It can be shown that if Δ t = t ₂ - t ₁ , a variation of the displacement ΔY along the Y axis is such that: $\mathit{\Delta Y}=K\left({\rho }_{\mathrm{<figure-callout\; id="2"\; label="b\; d\; b"\; filenames="imgf0006.png"\; state="\{\{state\}\}">b</figure-callout>}}{d}_b^{}{}_2^{}-{\rho }_{\mathrm{To}}{d}_{\mathrm{To}}^2\right)\mathit{\Delta t}$

, with :

• ρ _b : density of the second type of particles;
• d _b : diameter of the second type of particles;
• ρ _a : density of the first type of particles;
• d _a : diameter of the first type of particles;
• K is a constant, equal to 34.7.

In this example, the property of each particle considered is the aerodynamic diameter, corresponding to the product of the diameter of a particle by the square root of its density.

For an acquisition frequency of 10 Hz or 4 Hz, Δ Y is respectively equal to 12.4 and 31 μm, ie 5.6 and 14.1 pixels, for the first type and the second type of particles.

On each reconstructed image, the particles with a diameter of 2 μm appear more clearly than the particles with a diameter of 1 μm: thus, the signal-to-noise ratio corresponding to the particles of large diameter is greater than the signal-to-noise ratio corresponding to the particles of small diameter.

When establishing the potential displacements Δ (step 200), a displacement is considered as potential when the signal-to-noise ratio associated with the two positions, defining the displacement, is close. A signal-to-noise ratio S _Δ can then be assigned to each displacement Δ, this ratio being obtained by an average of the signal-to-noise ratios respectively associated with each position forming the displacement. The signal-to-noise ratio S _Δ of the displacements of the first type of particle (particles of diameter 1 μm) is lower than the signal-to-noise ratio of the displacements of the second type of particles (particles of diameter 2 μm). Moreover, the displacement, along the vertical axis Y, of the first type of particles is less than the displacement, along the same axis, of the second type of particles.

From each displacement ΔY determined along the Y axis, the modeled particle displacement for the second type of particles has been subtracted.

• les déplacements correspondant au deuxième type de particules sont regroupés selon un groupe Ga, centré sur la coordonnée ΔY = 0.
• les déplacements correspondant au premier type de particules sont regroupés selon un groupe Gb, s'étendant autour de la coordonnée ΔY = 12.5 µm.

There figure 5 represents the displacements ΔY of each particle after the subtraction of the particle displacement model, according to the signal-to-noise ratio attributed to each displacement. The acquisition frequency is 10 Hz. Each point of the figure 5 represents a validated move. We observe a segmentation of movements:

• the displacements corresponding to the second type of particles are grouped according to a group Ga, centered on the coordinate ΔY = 0.
• the displacements corresponding to the first type of particles are grouped according to a group Gb, extending around the coordinate ΔY = 12.5 µm.

It is also observed that the displacements associated with the first type of particle have a signal to noise ratio S _Δ lower than the displacements associated with the second type of particle.

This variant makes it possible to count particles according to a property, such as mass, charge, aerodynamic diameter. It can also be implemented to discriminate bacteria according to their motility. It is then possible to discriminate between bacteria of the Staphylococci type (non-motile which follow the fluid) and bacteria of the Escherichia coli type (motile, which move relative to the fluid).

In the embodiments described above, the images are acquired by an image sensor 20 placed in a lensless imaging configuration, no imaging optics being disposed between the image sensor and the fluidic chamber. Indeed, such a device allows a determination of three-dimensional positions of particles using a two-dimensional image sensor, by implementing inexpensive instrumentation. Such a device is therefore particularly suited to the implementation of the invention. But the invention applies to other imaging configurations making it possible to obtain positions of particles at two successive instants, and in particular three-dimensional positions. The embodiments described above apply to a defocused image sensor, forming a defocused image of the sample, according to the known principle of digital holographic microscopes. The advantage is to be able to observe particles of smaller size, to the detriment of a reduced field of observation. It is also possible to obtain the three-dimensional positions of particles by other imaging methods, implementing several image sensors. These sensors can for example extend parallel to one another, the three-dimensional position of the particles being obtained by stereovision. Two sensors extending along different planes, for example perpendicular to one another, can also be envisaged.

The invention can be applied to the detection of solid particles in the air, for example pollutants or dust, but also to the detection of particles, in particular biological particles, in a liquid. It will find applications in applications related to the control of fluids, for industry, the environment, health or the food industry.

Claims (15)

1. Method for counting moving particles (10a), the particles being borne by a fluid (10b), the fluid flowing through a fluidic chamber (15), the particles being entrained by the movement of the fluid, the method comprising the following steps:

   a) placing the fluidic chamber (15) between a light source (11) and an image sensor (20), the image sensor lying in a detection plane (P ₀);

   b) illuminating the fluidic chamber with the light source, the light source emitting an incident light wave (12) that propagates along a propagation axis (Z), and acquiring, with the image sensor (20), a first image (I,I(t ₁)) representative of an exposure wave (14) to which the image sensor (20) is exposed, the image sensor comprising various pixels, each pixel being associated with a radial coordinate (x,y) in the detection plane (P ₀);

   c) on the basis of the acquired first image, obtaining three-dimensional coordinates ((x,y,z)(t₁)) of particles, in the fluidic chamber, at a first time (t ₁);

   d) obtaining three-dimensional coordinates ((x, y, z)(t ₂)) of particles in the fluidic chamber at a second time (t ₂), subsequent to the first time;

   e) on the basis of the coordinates of the particles obtained at the first time and at the second time, determining potential movements (Δ) of the particles between said times;

   characterized in that the method also comprises the following steps:

   f) taking into account a model (mod) of the movement of the fluid in the fluidic chamber;

   g) on the basis of the model of the movement of the fluid considered in step f), validating movements among the potential movements (Δ) calculated in step e);

   h) on the basis of the movements (Δ_v) validated in step g), determining a number (N) of particles and/or coordinates of the particles at the first time and/or at the second time.
2. Method according to Claim 1, wherein step c) comprises:

   - obtaining a first image of interest (I_v (t ₁)) from the first image (I,I(t ₁)) acquired in step b) and applying a digital propagation operator (h) to the first image of interest in order to propagate it, by at least one reconstruction distance (z_j ), along the propagation axis, so as to obtain at least one reconstructed complex image (Az_j Y,

   - on the basis of each reconstructed complex image (Az_j), obtaining radial coordinates (x,y) of particles in the fluidic chamber at the first time.
3. Method according to Claim 1 or Claim 2, wherein the particles occupy various transverse coordinates along the propagation axis Z, and wherein step c) comprises the following substeps:

   ci) obtaining a first image of interest (I_v (t ₁)) from the first image (I,I(t ₁)) acquired in step b), and applying a digital propagation operator (h) to the first image of interest in order to propagate it, by a plurality of reconstruction distances (z_j ), along the propagation axis (Z), so as to obtain a first stack of reconstructed complex images, called the first stack of images, containing as many reconstructed complex images (Az_j) as there are reconstruction distances, each reconstructed complex image (Az_j ) being representative of an exposure light wave (14) to which the image sensor (20) is exposed;

   cii) for at least one radial coordinate (x,y) defined in the first image of interest, determining a reconstruction distance (z_x,_y) that maximizes the variation in a component (comp(Az_j (x, y)),-Re(Az_j (x, y)) of each complex image (Az_j ) forming the first stack of images, along an axis parallel to the propagation axis and passing through said radial coordinate, said reconstruction distance (z_x,y ) thus determined forming a transverse coordinate associated with said radial coordinate, the value of the component calculated at said reconstruction distance being what is called a maximum value A_max (x,y) associated with said radial coordinate (x,y), substep cii) being carried out for all or some radial coordinates associated with the pixels of the first image of interest;

   ciii) establishing a list of three-dimensional positions, each three-dimensional position comprising a radial coordinate (x,y) and the associated transverse coordinate (z_x,y ) determined in substep cii), each three-dimensional position being associated with the maximum value (A_max(x,y)) obtained in substep cii);

   civ) selecting three-dimensional positions (x, y, z_x,y ) depending on the maximum value (A_max(x,y)) that is associated therewith;

   the method being such that, in step cii), the considered component (comp(Az_j (x, y)) comprises the real part, or the imaginary part, or the modulus, or the phase of each complex image (Az_j) forming the stack of images.
4. Method according to Claim 2 or Claim 3, wherein the first image of interest (I_v (t ₁)) is:

   - the first image (I(t ₁)) acquired in step b);

   - or the first image (I(t ₁)) acquired in step b), from which is subtracted an image of the fluidic chamber, acquired by the image sensor, prior or subsequently to the acquisition of the first image, the subtraction being weighted by a weighting term;

   - or the first image (I(t ₁)) acquired in step b), from which is subtracted an average of images acquired prior and subsequently to the acquisition of the first image, respectively.
5. Method according to either one of Claims 3 to 4, wherein substep civ) comprises:

   - forming an image (A_max ), called the first maxima image, each pixel of which is associated with a three-dimensional position (x,y,z_xy) determined in substep ciii) and is assigned the maximum value (A_max(x,y)) determined, in substep ciii), for said three-dimensional position;

   - selecting, in the first maxima image, pixels (x_max ,y_max ) the value (A_max(x,y)) of which is maximum in a neighbouring zone defined around each pixel;

   - calculating, for each selected pixel, a signal-to-noise ratio (SNR(x_max,y_max )) depending on said maximum value (A_max(x,y)) and on the value of pixels of the first maxima image (A_max) located in a calculation zone lying around said pixel;

   such that each three-dimensional position is selected depending on the signal-to-noise ratio calculated for the pixel of the first maxima image that is associated therewith.
6. Method according to any one of the preceding claims, wherein step d) comprises acquiring, with the image sensor (20), a second image (I(t₂)), each pixel of which is associated with a radial coordinate (x,y) in the detection plane (P ₀).
7. Method according to Claim 6, wherein step d) comprises the following substeps:

   di) obtaining a second image of interest (I_v (t ₂)) from the acquired second image (I(t₂)) and applying a digital propagation operator (h) to the second image of interest in order to propagate it, by a plurality of reconstruction distances, along the propagation axis (Z), so as to obtain a second stack of reconstructed complex images (Az_j), called the second stack of images, containing as many reconstructed complex images as there are reconstruction distances (z_j ), each reconstructed complex image being representative of an exposure light wave (14) to which the image sensor (20) is exposed at the second time;

   dii) for at least one radial coordinate (x,y) defined in the second image of interest, determining a reconstruction distance (z_xy) that maximizes the variation in a component of each complex image forming the second stack of images, along an axis parallel to the propagation axis and passing through said radial coordinate, said reconstruction distance forming a transverse coordinate associated with said radial coordinate, the value of the component calculated at said reconstruction distance being what is called a maximum value associated with said radial coordinate, substep dii) being carried out for all or some radial coordinates associated with the pixels of the second image of interest;

   diii) establishing a list of three-dimensional positions, each three-dimensional position comprising a radial coordinate (x,y) and the associated transverse coordinate determined in substep dii), each three-dimensional position being associated with the maximum value obtained in substep dii);

   div) selecting three-dimensional positions depending on the maximum value that is associated therewith;

   the method being such that, in step dii), the considered component comprises the real part, or the imaginary part, or the modulus, or the phase of each complex image (Az_j ) forming the stack of images.
8. Method according to Claim 7, wherein, in substep step di), the second image of interest (I_v (t ₂)) is:

   - the acquired second image (I(t₂)),

   - or the acquired second image (I(t₂)), from which is subtracted an image of the fluidic chamber, acquired by the image sensor, prior or subsequently to the acquisition of the second image, the subtraction being weighted by a weighting term possibly comprised between 0 and 1;

   - or the acquired second image (I(t ₂)), from which is subtracted an average of images acquired prior and subsequently to the acquisition of the second image, respectively.
9. Method according to Claim 7 or Claim 8, wherein substep div) comprises:

   - forming an image (A_max), called the second maxima image, each pixel of which is associated with a three-dimensional position (x, y, z _x,y) determined in substep diii) and is assigned the maximum value (A_max(x,y)) determined, in substep diii), for said three-dimensional position;

   - selecting, in the second maxima image, pixels (x_max ,y_max ) the value (A_max (x, y)) of which is maximum in a neighbouring zone defined around each pixel;

   - calculating, for each selected pixel, a signal-to-noise ratio (SNR(x_max,y_max )) depending on said maximum value (A_max(x, y)) and on the value of pixels of the second maxima image (A_max) located in a calculation zone lying around said pixel;

   such that each three-dimensional position is selected depending on the signal-to-noise ratio calculated for the pixel of the second maxima image that is associated therewith.
10. Method according to any one of Claims 1 to 5, wherein:

    - step b) comprises two successive illuminations of the fluidic chamber with the light source, at the first time and at the second time, such that the first image (I) represents the exposure wave (14) at each of the times;

    - steps c) and d) are merged into one and the same step of obtaining the coordinates of particles at the first time and at the second time.
11. Method according to any one of the preceding claims, wherein step g) comprises determining a movement range using the movement model taken into account in step f), the potential movements being validated when they are comprised in said movement range.
12. Method according to any one of the preceding claims, wherein:

    - the fluid contains particles, each particle having a property and moving, with respect to the fluid, according to a movement model, called the particulate movement model;

    - the particulate movement model depends on said property of the particles,

    the method comprising, on the basis of movements validated in step g), a step i) of taking into account at least one particulate movement model, so as to count the particles depending on a value of said property.
13. Method according to Claim 12, wherein the property is a mass or an electric charge, or an aptitude to move in the fluid.
14. Method according to either one of Claims 12 and 13, comprising:

    - taking into account a particulate movement model for a preset value of the property;

    - calculating discrepancies in the movements of each particle with respect to said particulate movement model;

    such that the property of each particle is determined depending on said discrepancies and said preset value of the property.
15. Device for counting particles flowing through a fluidic chamber, the device including:

    - a light source (11) configured to illuminate the fluidic chamber (15);

    - an image sensor (20) lying in a detection plane (P₀), the fluidic chamber being interposed between the image sensor and the light source, the image sensor being configured to acquire at least one image of the fluidic chamber illuminated by the light source,

    - the device comprising a processor (30) able to implement steps c) to h) of a method according to any one of Claims 1 to 14 on the basis of at least one image acquired by the image sensor.

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